Two analysis examples on NFT and Helium
Thomas de Marchin
14DEC2022
Several Tb of data
Data are stored sequentially, requires developing specific tools to follow a transaction.
The structure of a transaction is difficult to read
Fragmentation of blockchain technologies
Two examples implemented in R:
OpenSea: big NFT market place
Weird Whales are managed by a specific smart contract on the Ethereum blockchain
To make it easier to extract information from the blockchain, we can read the events: dispatched signals (easy to read) the smart contracts can fire.
resEventTransfer <- GET("https://api.etherscan.io/api",
query = list(module = "logs",
action = "getLogs",
fromBlock = fromBlock,
toBlock = "latest",
address = "0x96ed81c7f4406eff359e27bff6325dc3c9e042bd",
topic0 = "0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef",
apikey = EtherScanAPIToken)) Where is the sales price? On OpenSea, sales are managed by the main contract and if approved, the second contract is called (here Weird Whales), which then triggers the transfer \(\rightarrow\) need to download all the transactions from the OpenSea main smart contract address and then filter for the ones related to Weird Whales (~ 10000 API calls, can take several hours…).
Networks are described by:
Each color represents a unique token ID
Made with the network and ggraph packages
About 2/3 of the transactions happened very shortly after the NFT’s creation
Helium is a decentralized wireless infrastructure for IoT devices (environmental sensors, localisation sensors to track bike fleets,…). It is a blockchain that leverages a decentralized global network of Hotspots. People are incentivized to install hotspots and become a part of the network by earning Helium tokens, which can be bought and sold like any other cryptocurrency.